{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1f900f30",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>男</td>\n",
       "      <td>4'13</td>\n",
       "      <td>8.88</td>\n",
       "      <td>195.0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
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       "      <td>170.0</td>\n",
       "      <td>72.6</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4'16</td>\n",
       "      <td>7.70</td>\n",
       "      <td>225.0</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>3133</td>\n",
       "      <td>174.0</td>\n",
       "      <td>52.7</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4'09</td>\n",
       "      <td>8.45</td>\n",
       "      <td>218.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>3901</td>\n",
       "      <td>169.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4'21</td>\n",
       "      <td>8.05</td>\n",
       "      <td>206.0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>4946</td>\n",
       "      <td>183.0</td>\n",
       "      <td>79.7</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3'44</td>\n",
       "      <td>7.52</td>\n",
       "      <td>210.0</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>3538</td>\n",
       "      <td>171.0</td>\n",
       "      <td>54.7</td>\n",
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       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>471</td>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4'58</td>\n",
       "      <td>8.76</td>\n",
       "      <td>200.0</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>4533</td>\n",
       "      <td>169.0</td>\n",
       "      <td>51.3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>472</td>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4'23</td>\n",
       "      <td>8.27</td>\n",
       "      <td>208.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4647</td>\n",
       "      <td>176.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>473</td>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5'19</td>\n",
       "      <td>9.55</td>\n",
       "      <td>210.0</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>7042</td>\n",
       "      <td>177.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>474</td>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3'25</td>\n",
       "      <td>7.50</td>\n",
       "      <td>252.0</td>\n",
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       "      <td>4'39</td>\n",
       "      <td>7.81</td>\n",
       "      <td>208.0</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>5688</td>\n",
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       "<p>473 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "     index  班级 性别 男1000米跑  男50米跑    男跳远  男体前屈  男引体  男肺活量     身高    体重  BMI\n",
       "0        0   1  男    4'13   8.88  195.0    12    1  2785  170.0  72.6    0\n",
       "1        1   1  男    4'16   7.70  225.0    11    7  3133  174.0  52.7    0\n",
       "2        2   1  男    4'09   8.45  218.0    14    1  3901  169.0  46.5    0\n",
       "3        3   1  男    4'21   8.05  206.0    13    1  4946  183.0  79.7    0\n",
       "4        4   1  男    3'44   7.52  210.0    13    9  3538  171.0  54.7    0\n",
       "..     ...  .. ..     ...    ...    ...   ...  ...   ...    ...   ...  ...\n",
       "468    471  17  男    4'58   8.76  200.0    12    9  4533  169.0  51.3    0\n",
       "469    472  17  男    4'23   8.27  208.0    10    0  4647  176.0  69.5    0\n",
       "470    473  17  男    5'19   9.55  210.0    15    6  7042  177.0  76.0    0\n",
       "471    474  17  男    3'25   7.50  252.0    13   13  5755  181.0  65.0    0\n",
       "472    475  17  男    4'39   7.81  208.0    14   11  5688  172.0  51.7    0\n",
       "\n",
       "[473 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1、对男1000米跑、 男引体进行等宽分箱操作， 分成 3 份， 并使用饼图绘制百分比\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "test_m = pd.read_excel('./18级高一体测成绩汇总.xls')\n",
    "\n",
    "test_m.drop_duplicates(inplace=True)\n",
    "test_m.reset_index(inplace=True) # 行索引重置：0~最后，从0开始编号\n",
    "display(test_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fe55ff98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 男1000米跑数据类型转换\n",
    "def convert(x):\n",
    "    if len(str(x)) > 1:\n",
    "        return x\n",
    "    else:\n",
    "        return str(x)+'\\'0'\n",
    "# 男 1000 米跑 变成 float 类型的值\n",
    "# 获取分钟数\n",
    "test_min = test_m['男1000米跑'].map(convert).str.extract(r'(\\d+)\\'(\\d+)').applymap(lambda x:int(x))[0]\n",
    "# 获取秒转换为分钟数\n",
    "test_sec = test_m['男1000米跑'].map(convert).str.extract(r'(\\d+)\\'(\\d+)').applymap(lambda x:int(x))[1] / 60\n",
    "test_m['男1000米跑'] = test_min + test_sec.round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ee429d86",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>班级</th>\n",
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       "      <th>0</th>\n",
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       "      <td>4.2</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.3</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.2</td>\n",
       "      <td>8.45</td>\n",
       "      <td>218.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>3901</td>\n",
       "      <td>169.0</td>\n",
       "      <td>46.5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.4</td>\n",
       "      <td>8.05</td>\n",
       "      <td>206.0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>4946</td>\n",
       "      <td>183.0</td>\n",
       "      <td>79.7</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.7</td>\n",
       "      <td>7.52</td>\n",
       "      <td>210.0</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>3538</td>\n",
       "      <td>171.0</td>\n",
       "      <td>54.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  班级 性别  男1000米跑  男50米跑    男跳远  男体前屈  男引体  男肺活量     身高    体重  BMI\n",
       "0      0   1  男      4.2   8.88  195.0    12    1  2785  170.0  72.6    0\n",
       "1      1   1  男      4.3   7.70  225.0    11    7  3133  174.0  52.7    0\n",
       "2      2   1  男      4.2   8.45  218.0    14    1  3901  169.0  46.5    0\n",
       "3      3   1  男      4.4   8.05  206.0    13    1  4946  183.0  79.7    0\n",
       "4      4   1  男      3.7   7.52  210.0    13    9  3538  171.0  54.7    0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_m.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e210df5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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lnODEyRhzGm5IlMWVgn+Ge+7HAx+z1g42X8FIXIX7EX3CDj2+/N+4H/VP0Pck6Qzg9GAeg7sH3LIfa21bz//GmN1wNS4HW2t7TrKexCWVw3HV2K/gOmS+aoMOmcEY9L1x1ctluCr2EZV8jTETcLPGlQLHWmtfGWaT/s/jLWPM13Anj+Ca2e4xxvwX+EGqr0dSPDviOvJ14U6+epaX4vr0TAwWfda4iwfNwb13E4Flxphv2xRGDhg3he5B9NYoQlLCD046LsGNGijAlaSfCm5X4eYp+CA4YbXJv0ujFcxzUGGTxuMDj+BqHi7FvUefAz6Fq/07xg7fmVjSxXebQr7eSBr7Hdx/HlelHM3gMW9ikLHfKW7/DVxSGrTTXb/1d8FVuX4nuN8zBOr0AdY1uB+mpcF2FtdWuGnwuhyYhuf/DWCP4P8Tg2NsF9wscFLw2DP0HQN/Ia5KP3lf/wZeH+Q4tbg25qbg73HB83skOM6JKcZ7cLB+Ojqr7Rvs6+oU1r0Fd/JTnrTsW/R2pBtRuz6uluoN4OvDrHc6rgr917gE/VLw3i/AJXmLK11+bATHrsI1VSwihT4QuOT32eD7+C7wI4IOi7gObevYeCz8AuCAFOMpx53UNCZ9HpYHz7M7+Mysp3f8+7eBU4PbU0nHPCyFY13Lxv1mOoFDku4fiWuLP5Z+Y+xxney+gGsKWo3r6zFjDJ/BrXDNMJsP8Nguwftkg+f8aTI4L4Rug7xHvgMIyw1X0rfAg0nLinClm1/iShd7jmH/xcGX7SdJyzYDLsB1btsshX30dDacksK6dbhOVf8vadlWwfaHDrD+F4LHTsMNx7I9P0y4DkKrgNljeP4R4OGk+z0l6sm4UrgF9gseexs3ZKln3YuBpn77mw8sHGEMcVzCLElx/ZIgAZwzkuMMsq/ZDJDwg+e/Z79lH+15L5KWXRks+9MIj1uXlKh+iWtLvh83ydOh/db9UrDeebge8tP6Pf5S8PjFKR57Ku7k7UH6dfbEVc/vEDzXb+GS42u4hPgeLgn3JNeFuPkAer6T2+FKzkfj2vf3J5goJoWYrsUl9uPp7QR7QvD57JkI62qCJu5+234qWH8Vw0yAE3x2WnFV7jOSnvOG79UA2xyEO8n4BW4k0VW4/jRH09tz/5ZUnucg8TyLa5oa6PHJuBqvBlwtR89InZ3H+tnXbQTvk+8AwnDDVWM1Bx/wK3Cd655KWtZz6wK+OspjfCXYx6vAH3ElmO6kfTcB5w+zj54kOeSsZ8HzuY9+JXl6S+679Fs+E9dueWNwP0LfhF+E+7F/H9hilM//TuCspPvXAK3B/58NjrdpcH99TyzB/d/hJuJJ3t/NwAcjjOEB4OUhHp+FG2t8D65ZYwvcicU9afiMzaBfwsclvPpg+VMEiT94vd8DHgvul+NKeB+Q4slK0jGuTvqMteBGXQxYW4M7ubVBrJNxJer9kh7/O0knZsMcdytcu/s3cdXVxbjaltdxSSX5e3UjLpnuCpQlPefGpHVG9b3rF9OPgn19Pbh/enB/s37rXc3ACf+IYP2bUzhWMe77/fekZT2dPw8ZZJvJ9JawC/o99n+MocSN6xhsGaTQQm8N0vnB/aOC7+GoZhXVbXQ3XTwnTYKZwk4wxnzCGHOuMeYHxphrg0lFluN+YNbQm/wuw7VtzsD1EO7E/XCN+HrZQZvhz4O7W+CqOX+Ea8euwrWlVgGXBO11g+lpxx6ud/OpwJettf0vq9nThr2ho1zQ3n0Vruf0ZxmAdZ2+zsW9Ng8ZY7Yb5vh9BJ2u9sH9sPfo6UEOrkf0Olz76ARcSSi5J307vZMg9Shi5D2HK+jX8ci4y+F+LWgPfhf4Ca5d+yHcazIfONwYs+kIj9VfT9ttsTHmCGPM33Cl3+XB8g/hmlB6Xu9fAfsbdzGl83Dtx7+1I+swBu6z3IKrNp5hrT3TWvvoIOv2vOZduNf708B/jTHfTtoXJHVmG8LBuPb6n1tru63rAxPFnYj+CVeV3XM9gmNxE928aIMOgdZNbNRzLYencSfJoxZ0IP0O8D1r7S+DxT19pFLtE2ODv8P2wQme7824WrkeE4fZpgFXA7XUbjyC4Y3g7ypGyLiL4HwZeMAOcG2KYGKti3Dfjb8EiycD9TbI/jI+1GkvfdpwPfE/jztzfSu4PYLr0LYfrur2pgG2XWKMacP9EP5gFMc+AHfGfzdwtnW94zcwxizAJcQfD/BFT5bSCaAdvENRT8JPnqnru7hxv/va3s5cA+3ziWA62vOAx4wxn7DWPjRcLEHv91/hZvdLnp3uDnqnLD4IV/K2xl2k6F36dgy7g41nAiumb8e+VFSw8Y/1frgq4heDeB6y1h6XFP9/cJ+d03Gd+UarMvh7HO57fT/wTWvtCmPMQlwP8OuT1r8C15TxC9zwqBZctf5IXQT8n7W2/wlTzzDFYts7nK7ns9cWvBdv4054H+73uGEY1tqNYrXWvoRrwuo5/qnBv08NFB+9J7hft0kdEEciOKH9SXDcT1trk0fb9IwUGegkaqBEVxP8HSjWjXdgbf/RLZMHXLGvDmD3YCjtUcCb1tqz6b2aYsqzSxpjNsOdqPfMkLdRR93Ar3G98L+QdEJZl3RMGS++qxjy/YZLJK249sMBJwLBfdEt8Ogoj7EXrlQz4AQZuOptC+w6zH56JlKZOMo4PklSHwBcj/3VwN791osE68Vw7b7/xJXIauh7AZnv0W/msn77MbgOaBbYa4DHKoLXvwvXW/liXHv+/mzc5mv63X8QaB7h818EXDLE43czwGQ+uCreRYywOr3fPo4NXofvDPDYe8DaAZb/mN4q7V+l8TM/B3fBpp/TdzKj7YNj9VSrP0wwiiK4/9tUPqcpxlBMbzv9gO3EuJOzjV6XERyjBldD8yiw/QCPXxYcv6Tf8quAruD/TXu+t0nPf1QXlcKNvLAM0sEQd4LTjasF+SaulqTntd852DblTsXBfno+P/UM8PuG+w2wJDWhBct/QAoXBgI+ka7PpW6q0h8P38NVDf/UDl66nh38HdX14a21z1hrz7N9J/dI1lN6fXuQx3tUBX9H+7moDf62G2OOwZVYD7PWPmWMmWTcNc9/i/uRBJeAv4brWHWytTZB7+VkC3HtiguMMWcaY/qUto27bvzfcUPi6m2/a4xbp5nevg3X4IaFPYIbG97Sf/1+z6WSEdSABcPL6ujbVNBfE26a0f5+hWvKOSvV4w1g9+Bv8jwHPSXQTeit2k+WfL2DsVZpFxljPmaMuRuXyOPW2m/avk0E0+gdCQCu093ztncIV8/np3wssQTODPZ/m3Ul//7xluOS7UjHmPdsvyeuL84V1toD7cDz4h8ErLYDN5P0fN52BhYZY5YQzF/AKH8HcM150LeGLVkd7kT4MuuaQh5Oeu0bktZJ1a1J/1/X//fNuMvjXoUbtXDOALH0n/tiIB8dQTwyDCX8DAra1j+PayPs396dbM/gbyYupgGuOneRHaJKPdBTJT/ai8b0/OB8CHf9731s70VkTsNNTrMbrloR3EQ1R+FqBM4BsNbegquWvgk3HKoEV2X4437HmoVrAwbXI3wjxpgtcFWtf7bWvm2t/Tfu8qynAo8YY2oH2i4wkY3H5g9lxyDWZmNMsTHmo8aYPxpjZiet0w4UGGM2N8Z82RhzqTFmM2vt/4Ln+72gj0HyczDGmM+ncPzjcCcUT/dbPh13wtmnmjiY/+GP9P7ofivF59lnH8aYY40xv8clzvm4E6mdrbW3DrDJTrgStQ1OkLak70yUPc0SI76IT7+4qnG1Ry2460MMZA4u+dWP8jCN1tqTrbV3DLHOZgz8nbYEz9G6Mf6HJD1Wjxu+Nhpzgr+DJfyeWThnGGO+YoyZb4x5KDh5WRM8NnXgTQf0Z1zNJbjS/gbGmCm4USsrcX0t+l+Bs5LeJowBBSerRxg3a6akg+8qhny94X78n8P96Ax54Rpcz2YLHJ6BOKpwP+rD9gQH/hXEsdE42hSPdXtwrMJh1tuBFMcaD7Of64P9fHyAxybgShZP03e8+WRcFbdlkOFfuBPhdQzQk3qIWC4N9rkM1/HpUZLGNONGMDyNS/qrcJ2ckudX3z547f7Sb7974K4cONSxe6pjfzzAYz1V/f9KWlaOmwL3A9wJ2OJgnQHHvzNAUwOub4JNuq1ikPnzk7b5AfBa8P8+wXYfGeD93HKMn4s/BPv58hDr9IxI+eVYjjVMHP/BTdzTf/kVBCNIkpZ9OHj/jxrD8V4NntOAY+mBzwSP3xq8RjHcSXNN8HhHKr8T/fa5Pe4aCskXT6rFDdF7CqgbZLu/Bp+7QXvpJ31G0nqNiTDfvAeQrzd6xzVfOMx6PcODuoHJGYjjw0Ecv0lh3XuDdbcaxXFKcdOaNqaw7sHBcY4c43OrCxJXTb/lm+JOtu7v/1jw+Idw1cqXJi2rSPq/Z/hYS4px7IxL5D3J7xfB+3oSbshfz8VSVuJ6cg94VTJ6r2Z3QtKyS3C954c6/h24tvDJwf1jceOcy+jtvxELHqsK3ucXCIaL4WoHLK6UN7vfvrfDzZ430HE/hzuhfX6wJNNv/YMIhg3ihnGtJmjPD5adhTthGvVQLVwTj8VVMQ+13mP0m4sg3TdcX5WN5rTA9VRf32+ZoV9flxEeawt6+74MeMJN79C5AS9UE7wfL47xOU8Pvnt/Sf5ODbDehQzRZyD4bj8RrPP5TL1HYbt5DyAfb0lfrKtSWPfcYN0X0nTsCG64Xy0u8fV8aU5NYdvng3W3GcVxdwu2HXB2un7r9kwwkokajWODZPZVhrhaWpAQt026vwuu5/6d9E5C8m6Kx+wpUXYAZwbLauidaW01rq/CoD+AwTaluGaMtbiTkiLcdcIHLeHjpsTtBk5JWlaW9L5bXMlxa1xb/m24JpL+Hcl+Faz7JjA1afl5wPFDHH/KcM+r3/oG1zN/PW4IW/JjRQQzJY7yvd8jeO2eoG+tTmG/9XpqRLpIcVbJNH9G/0xw+ew07vOU4DkNOncErsS9cojHl+Ca/kYbw964oYIfSmHdqbhaIRt85v+Iu4T4T4J9JM94+Ofxfo/y9eY9gHy64aqCe378/8IAveZxPdL/getU9ClcqdgCX0tTDAZXVWeTbg0klaSG2HYZQ5QAhtl2Iq7UfFsK6/b0Dt9rpMcZZr/b4kqro+rtjuu935L0ug3bizjY7vwg6X5qkH2mdMnfYP1dcW2w6+mdivQvg6y7d/Denj3AY9PoncL1J8GyoUY89Azls7gx2TsGy28jmJI4Te9ROa5T33ME1cC4kRo34vp9DBrjMPs9EFdTdif9pgbGNZ9cgzuJ2hp3uVrLGKahHuNrcBkpnkyOYJ89NXmPD/L4BNwJzl1D7GM5IxyZ0m/7Ec0UGnzWn+z3W9X/9hAwy8f7lI837wHkyw03zvROXGlu0KlScR3jnuv3oX6XEZSSUoznp0n7PyXFbVYywDCiERzzt8C5Kaz3V0ZZkzAO7+MFSa9bStW9uP4aY74WQNL+Phn8OPfEcccA6xyOG4o5Z4j9XIrrST7sdeGD9Wtwba8WVzX8JO5EZqMTilE+rxpc34bXSTqpxHXg6vnhf4MRTreKa5tejxsVMtBJdjG9c/Un13qMujZhjK/DHxhgeOYY9zkBVyoe7OSwZxa+7/dbfgxutMwRwWvS7eH12AI3fO883MnZubimmVH1JdJtiNfadwD5cMOVtP4bfHFSmYd+G3qvt74SNylNumOqxU1sceEItrmdMVTpjeA4nw0SSVpPctIUWzFu+OKbjLK0maY4TqG3tmGjaloyNCVp8Ll5qF9yHPMFTnD9Gd7F9eSvHeDxyUGytwwypnuQ/X4XV5s2ZMcuXC1M8nP6psf39lfA7RnYb5Qh+sUEr+9G1e309jca8LOmW/7cNNNeejTgSngpDSey1r5pjLkWNzHMN6y176c7IOtmNxvpdK1/p+/14zPlb7hkP9jwIW+stR3GmFNwF9NJxyWLRxvHDcEMiZfgOs71f9xm6LirjTFH4pqFvoGrvdh2tPszxhyMK7ElcE0ejw9y3AZjzNG4znSzcNMsD/u9sKlfWrhnut/VuO/caGYVTJdmeqf1TRtr7bxhVplrrR1oLo5zcUMm96TvbIySZ0yGfjckRxljaqybAEdCLpg69ZO4udfHJREE8xb8EDdNbVpPuIwxu+E6lbYMu3IGGWPOx9Vg/M5nHMmMMR/CTZN8ejaeiEt6KOGLiIyj4EJJ7dZdzEZk3Cjhi4iIhICm1hUREQkBJXwREZEQUMIXEREJASV8ERGREFDCFxERCQElfBERkRBQwhcREQkBJXwREZEQUMIXEREJASV8ERGREFDCFxERCQElfBERkRBQwhcREQkBJXwREZEQUMIXEREJASV8ERGREFDCFxERCQElfBERkRBQwhcREQkBJXwREZEQUMIXEREJASV8ERGREFDCFxERCQElfBERkRBQwhcREQkBJXwREZEQUMIXEREJASV8ERGREFDCFxERCQElfBERkRBQwhcREQkBJXwREZEQUMIXEREJASV8ERGREFDCFxERCQElfBERkRBQwhcREQkBJXwREZEQUMIXEREJASV8ERGREFDCFxERCQElfBERkRBQwhcREQkBJXwREZEQUMIXEREJASV8ERGREFDCFxERCQElfBEZEWPMBGPMrDFsW9NvWZkx5vvGmIlpCVBEBqSELyIjVQC8bIz55yi2PSvYtixp2Szg+8BnjDEXGGO+bYwpTkOcIpKkyHcAIpJbrLVrjDHPA+tGsp0xZhpwHvAA8G9jzCLgS8Bvgc8GqxUDr1hrO9IYsoighC8io7MceLfnTlAir7XWrhhoZWNMHXAd8C3gcWttY7D8m0AMOAW4z1p7W4bjFgktVemLyIgYY6YAZUDEGPMNY8wVwJvAYmPMvAHWLwLmAicCC4G3jDFbGmPOB/5lrX0C+AdwiTGmcNyeiEjIqIQvIkMyxjwMGOBhwAJrgUnAS8DfrLUrg+r6pcBuxhhjrbU921trO4Grg7urjTE7AiuBo4FJxpjFwGTgGGtt1zg9LZHQUcIXkeH8C/iKtfZ7PQuCpP2utXZlsKgVdzLwleRkn8wYsznwYyCC++3ZCmgDunFNBFXAgE0CIjJ2SvgiMpy7gZ8NsDy5Y90M4L/W2tcH24m19n3g0z33jTFXA5fiagamADONMfsA04BVwHVB7YCIpIEZ5GRcRGQDY8wqYDbQhCuh/xZoBD4I7u8MFFprt+u33f7AAUAJUINL7JW4wsZEoB3YFdeGvxZ3ElEYrHO9tfbFDD4tkVBRCV9EUvEs8FegAVf9Xge8A9yLK6Gvwp0A9PcMsAmuD8BrwPvW2j7D+YwxTwKPAK8Aa5KaCUQkjVTCF5FhGWM+i6tibwnuXw3cY629bgT7OB9Xyi8BqnEnDeXA3rgTiZVAC3CetXZpWp+AiKiELyLDs9b+tf+i4DYSdwOHAXsAPwcWWWtbjTFXAldaax8be6QiMhiNwxeR0bDAiIbQWWvfttZeDuwAHGatbQ0e0th7kXGgEr6IjEYXI0z4SU4DpifdL0eFD5GM05dMREajEzf2fsSste9ba580xuxhjCkHtkWlfJGMUwlfREajFTeMbixqcT3+K3BT84pIBinhi8hoLMQNsxs1a+19xpgLgKnW2sXpCUtEBqNheSIiIiGgNnwREZEQUMIXEREJASV8ERGREFDCFxERCQElfBERkRBQwhcREQkBJXwREZEQUMIXEREJASV8ERGRENDUuiI5KBKNVwETgOrgb///y3An9Ca4DfQ/uDnxW5JuzUBT0m0t0Fg/b+768XheIpI5mlpXJEtEovFiYFPcpWMHu20KTGT8a+fWA8twF7tZPsD/S4CF9fPmNoxzXCKSIiV8kXEUicYNsDmwTXDbNun/WeR+M9tq3IV13u53W1g/b+4yn4GJhJ0SvkiGRKLxqcAewO7ALsB2wNZAuc+4PGoCFgAvAC8Gf1+unze3zWdQImGhhC+SBpFofBNcck++zfQaVG7oxF1mN/kk4Bn1GRBJPyV8kVGIROPbA4cEt/2AGT7jyTOduMT/CPAo8Jj6BoiMnRK+yDCCdvfkBH8QMNVjSGFjcbUAPScAj9TPm7vIb0giuUcJX2QAkWh8GvBh4GjgYKDOb0TSzxtAHLgDdwLQ4TkekaynhC8SiETjuwHHB7c96B2rLtmtCbgPl/zvqJ83d4nneESykhK+hFYkGi8DDscl+A+jdvh88SKu9D+/ft7c5z3HIpI1lPAlVCLReAlwLHAaLslX+I1IMuxN4Ebg+vp5c1/zHYyIT0r4kveCTncH45L8icAkvxGJJwtwyf+G+nlzF/oORmS8KeFL3gra5D8FnIzGxEtfzwLXA9fUz5u70ncwIuNBCV/ySiQarwXOBD6HG0onMpR24D/AX4D76ufN1Q+i5C0lfMkLkWh8H+A84CTCO3WtjM07wBXAVZr3X/KREr7krOASsZ8GzgV29RuN5JFO4DZcqf/u+nlzuz3HI5IWSviScyLR+A7Al3Cd8Ko9hyP5bSHwa+DK+nlzmz3HIjImSviSMyLR+EHAt4Dj0KQ4Mr4agcuA36u6X3KVEr5ktWBI3UdxiX4fv9GI0Ab8A/hl/by5r/oORmQklPAlKwUT5JwBfAPY1nM4Iv1Z4E7gF/Xz5j7oOxiRVCjhS1aJROMVwPnAV4FNPYcjkopHge/Vz5v7sO9ARIaihC9ZISjRnwd8B9jEczgio3E/LvE/4TsQkYEo4YtXkWi8EDdRzveBWZ7DEUmHu3CJ/1nfgYgkU8IXL4LOeCcBl6A2eslPtwLfr58393++AxEBJXzxIBKNHwf8CE2WI/nP4nr1R+vnzV3sOxgJNyV8GTeRaHx74DfAEb5jERlnzcBPgZ/Xz5vb4jsYCSclfMm4SDQ+EVd1/0WgyG80Il59AFxUP2/u9b4DkfBRwpeMiUTjBcA5wA+BOs/hiGSTJ4Cv1M+b+4zvQCQ8lPAlIyLR+P7Ab4HdfccikqUscC3wLU3XK+NBCV/SKhKNbwL8EviU71hEcsQa3NTRV9TPm6sfZMkYJXxJm0g0fhZwKVDrORSRXPQw8Pn6eXPf9B2I5CclfBmzSDQ+C/gzcLTvWERyXCuuz8vP6ufN7fAdjOQXJXwZtWDynPOBnwBVnsMRyScLgHPq58192ncgkj+U8GVUItH4tsAVwAG+YxHJU93A74Fv18+b2+w7GMl9SvgyIsFQu28CMaDMbzQiofAm8CnNzS9jpYQvKYtE45vhpgk90HcsIiHTgTvJnlc/b26351gkRynhS0oi0fiJwF9QD3wRnx4FTq+fN/c934FI7lHClyFFovFK3Pz3n/Udi4gAkAAuqJ8391rfgUhuUcKXQUWi8d2A69Hla0Wy0Q3AF+rnzV3jOxDJDUr4spFguN1XccPtSjyHIyKDew84sX7e3Od8ByLZTwlf+giubHctMNdzKCKSmjbg/Pp5c//qOxDJbkr4skEkGt8BuAXY2nMoIjJyf8Ul/jbfgUh2UsIXACLR+CeAq9CMeSK57FngE+rFLwNRwg+5YCKdHwMX+Y5FRNKiATitft7ce3wHItlFCT/EItH4JOA6dNEbkXzTDVwM/EiX3JUeSvghFYnGdwZuBrb0HYuIZMwNwFlq1xdQwg+lSDR+Aq5kX+k7FhHJuP8CH62fN3eV70DErwLfAcj4ikTjX8aV7JXsRcJhf+CJSDS+je9AxC+V8EMi6Jz3K+DLvmMRES8acSX9R30HIn4o4YdAJBovx1Xhf9RzKCLiVztwdv28uf/wHYiMP1Xp57mgJ/59KNmLiJsq+9pINH6x70Bk/KmEn8ci0fgs4C5gO9+xiEjWuQw3M5+SQEgo4eepSDQ+B1eyn+47FhHJWtcAn6mfN7fLdyCSeUr4eSgYY38fUOc7FhHJevOBU+vnzW33HYhklhJ+nolE43sCdwOTfMciIjnjbuBj9fPmtvgORDJHnfbySCQa3xdXsleyF5GROBq4OxKNT/AdiGSOEn6eiETjBwP3ADW+YxGRnHQgcH8wskfykBJ+HohE40cCd6JL24rI2OwJPByJxtX/Jw8p4ee4SDQ+F7gNKPcdi4jkhR2B+1TSzz/qtJfDItH44UAcKPUdi4jkneeBw+vnzV3jOxBJDyX8HBWJxvfBddDTRXBEJFOeAo6snze3yXcgMnaq0s9BwTj7O1CyF5HM2hu4Pbgeh+Q4JfwcE4nGZ+N649f6jkVEQuEg4KZINF7sOxAZGyX8HBKJxjfDVeNv4jsWEQmVY3EX3VHOyGF683JEJBqfCtwLbO47FhEJpZNwF9yRHKWEnwOC2a/uBrb1HYuIhNrnI9F41HcQMjrqpZ/lItF4EW7o3VG+YxERASxwWv28uTf4DkRGRiX87Pc7lOxFJHsY4OpINH6A70BkZJTws1gkGv8qcJ7vOERE+ikF/hOJxrfxHYikTlX6WSoSjZ8A3IxOykQkey0E9q2fN3el70BkeEomWSgSje8GXIfeH8kytquDjtVL6W5bn9L63a3rsJ3tGY5KPNoKuFUT8+SGIt8BSF+RaHwG7mI4mkVP+mh68S4a7/49sy66HYDujlZW33c5LQufobu9meJJM5mw9yeonHPgqI/R8s5zrPjXxUw54aKN9pN46t8kHr8R294CBYVU7nAYk4/6IqaomK7mBCvn/5DOplXUfSRK6XQ3oCTx5L+p2vUYiidOG/0Tl2y3D/A33LA9yWIqQWaRSDReiUv2M3zHItmla91q1jx0VZ9lq++7nOa3n6Z6r49Qe+hnKSifwKpbf0rz20+N6hjdHa003vNHymbtvFGyX//6Y6x5+O9MPPDTbPqZ3zHxgE+xfsF9rH32FgDWLbiP7o5WSqdvR+Jx13nbdrbTtX61kn04fDISjX/LdxAyNCX87HI5sJvvICT7NN73J7rbmjfct12drHvlAWoPOYuavT9B9W7HMfWkSyiaOI31rzw0qmMkHruOzqYGJh3xhQEe+wfVexzPhD0/QsnULajZ9yTKZu1My9vPANC5einlW+5B1U5H0LF6KQDrX3uUyjkHjSoWyUk/Dq7gKVlKCT9LRKLxC4DTfMch2af57adofuNxqnY+csOy7tYm6OrEFPVeGdmYAigoxBSNfMrz9hXvsvbZ/zBhr49QPGWzPo/Z7i5qD/0sE/Y+se9GhUVgTM9KGFMIBYVguwBoefc5yrbYfcSxSM4qBG6IROOaDTRLKeFngUg0vjfwS99xSPbpbmum8Z7LqNrtOEpnbr9heWFlLcVTNifxxI10NCyiu62ZxJP/prNxMRWz9x3RMay1NNz1ewqKyyiduT0tC5+hq2XthsdNQSHlW+1JUdWkDcvaV9bT+t5LlG3hKqQKKmtpX/Ue7cveprCilralb1I6bWtMzwmBhMUUYH4kGi/zHYhsTJ32PItE41OAfwElvmOR7LPmkb8DhtqDz6T5zcf7PDb1Exez7NpvseSKnqkaDLWHf56KbUaW8Ne//ADtS98AoOGO32A7WsEUMOno86na4dA+67avfI+mZ//D+tcepnzWLtR8yJX6K7c7gGX/uJWWt55i0jFfYt2C+5h44OnYrk5MoX5mQmYP4I/A2b4Dkb5UwvcouPLUdcBmw60r4dO2+HWaXriDyUefT0FpxUaPr33mP3Sta6R8q72o3OFQCsoqWfvkv2hb8kbKx7DWknj8eigoYuonLmazL1/HzAuupXz23jTEf0Xbsrf7rN/dto72le9iO9qgoBDb3QlAydQtmPG5y5l+zmVUbLMPxhianruV93/5cRru/O3YXgjJRZ+JROOaNCzLKOH7dQlw5LBrSejYrk4a7vodldsfTPlWe270ePvyhTQ9dytTTvgmUz9xMVM+/HWmn/MnTHEZDXf8JuXjdDYuonPNMqp2PIzyrfYCoKC0gslHnQ/W0vzaI33WL5u5A5ue8Ss2OfUntNa/yOoH/rrhscKqWoonz2TdS/dSPntfEo/fyKSjvsj61x6mfWX96F4IyWW/CZorJUso4XsSicaPA/6f7zgkO619ej6dTauo2fdkupoTdDUn6G5vAaCrOUHzW09RUFbdpxd8YeVEKnc8jI6G9+lqXZfScbpa3Hqlm+3YZ3lBaQUFFTV0NjUMuF3Z5jtRMecgmt/4b5/l1nbTvuIdt315NdW7HkPx5M3paFyc8nOXvFECXB9c7VOygBrXPIhE49OBa3AXoRDZSMu7z2Pb1ie1z/da9LtPYUrKAYO1tk/HONvR5v7p7krpOIVVte6ffp3rbHcX3a1NFFZMoKs5wdqn5zNhr49RWDmxd9vyamy/47S88xzlW+6J7WzHFLpuKaaopDcuCZstgD8Ap/sORJTwx10kGjfA1cCkYVaVEKs97By6+5XSW999nrVPz2fqyT+kY8U7rH7wSloWPkPF1h8CoLOpgfWvPEBh1WQKynsLVd3trRSUDNxpunjiNAonTKX59cf6dNBb//L90N1F2axdKCitoOmFO7GdHUw64vOAOyFoeefZDTPq9Wh+/TEmH30B7SvrsZ0uyduOVgqK1Wk7xD4dicbvrJ839zrfgYSdEv74+zJqt5dhlE7beqNlXU2rACiP7Erp9O1Y++xtrJz/Q5d0C4tpX/oGtqONSUdfsKHUv/rBK1n73G3MOPcKiqonD3isiQedTsPtl7Lipv+jfMs96WhYRNMLd1Cy6TaUb/0hjCmgZr+TWfPQVXS3rKV05vY0v/kEHY1LmHT0BRv207FmGYXVUzBFxRRPmkF323rW/Pd62le9v9HYfgmdP0ai8cfr582t9x1ImCnhj6NINL4DMM93HJL7CkrK2OS0n7DmwStpfX8B3e3NFFZPoXrXY6ja5egN65nCYgoramCIq2JW7XAoBaUVrH1qPqsfuhpTWETFdvsz6bDPucl8gAkf+jimqISm526j+e2nKJm6JZuc+iPKkuYGaHn7aaqDYxeUVlB7yGdIPH4DE/Y4nuLJSvghVwNcE4nGD6mfNze19iZJO10ed5xEovES4ClgV8+hiIj48v36eXN/4DuIsFIv/fHzfyjZi0i4fT8Sje/jO4iwUgl/HESi8YOAB9EJlojIQmDn+nlzm4ddU9JKCSjDgjGof0evtYgIwFa4Gk8ZZ0pCmTcPmOU7CBGRLHJhJBrfeApJyShV6WdQJBrfD3gMTbAjItLfS8Ae9fPmdvoOJCxUws+QSDReDFyOkr2IyEB2Br7lO4gwUcLPnG8BO/gOQkQki30/Eo1vO/xqkg6q0s+ASDQ+G1ddpflERUSG9ihwcP28uUpGGaYSfmb8CSV7EZFUHAhsfJUoSTuV8NMsEo2fBVzlOw4RkRyyFphdP2/uCt+B5DOV8NMoEo1PBn7hOw4RkRwzAfix7yDynRJ+ev0fMPAlyUREZCifiUTju/sOIp8p4adJJBrfHjjXdxwiIjmqAPiN7yDymRJ++vwSKPQdhIhIDjsgEo2f4juIfKVOe2kQicaPAe70HYeISB74ANhOF9dJP5XwxygSjRfhSvciIjJ2mwEX+Q4iHynhj93nge19ByEikke+GYnGN/cdRL5Rwh+DSDQ+EbjEdxwiInmmHA3TSzsl/LH5LjDFdxAiInno1Eg0ruuRpJES/ihFovHpwPm+4xARyVMFuLlNJE2U8Efv22i+fBGRTPqYJuNJHyX8UYhE4zOBz/mOQ0Qkzxngh76DyBdK+KPzHaDUdxAiIiFwbCQa3893EPlACX+EgqEin/Udh4hIiKiUnwZK+CP3/4AS30GIiITIoZFo/HDfQeQ6JfwRiETjEeAzvuMQEQkh9dgfIyX8kfkuUOw7CBGRENovEo0f4DuIXKaEn6JIND4DOMN3HCIiIfZN3wHkMiX81H0Zle5FRHw6PhKNb+c7iFylhJ+CSDRehbtIjoiI+GOAr/sOIlcp4afmbGCi7yBERITTI9H4NN9B5CIl/GFEovEC4Cu+4xAREcBNevZl30HkIiX84X0M2NJ3ECIissEXgqZWGQEl/OF9zXcAIiLSx0TgHN9B5BpjrfUdQ9aKROP7AE/4jkNERDayEJhdP2+ukliKVMIf2oW+AxARkQFtBWi63RFQwh9EJBqvw7Xfi4hIdjrXdwC5RAl/cGegi+SIiGSzj0Si8U18B5ErlPAHpw4hIiLZrRg3T4qkQJ32BhBcoOFR33GIiMiw3gG2Vue94amEPzCV7kVEcsOWwJG+g8gFSvj9RKLxGuCTvuMQEZGUqfNeCpTwN3YaUOE7CBERSdkJml9/eEr4G1N1vohIbikCTvIdRLZTwk8SicZ3BHb3HYeIiIzYqb4DyHZK+H2d7DsAEREZlX0i0fgWvoPIZkr4falKSEQkd53iO4BspoQfiETjuwLb+I5DRERGTdX6Q1DC76XqfBGR3LZTJBrfwXcQ2UoJv5eq80VEcp9K+YNQwgci0fieuNmaREQkt6kdfxBK+I5K9yIi+WGrSDS+h+8gspESvqOELyKSPz7sO4BsFPqEH/TOn+U7DhERSRsl/AGEPuEDx/oOQERE0moPza2/MSV8JXwRkXxjgON8B5FtQp3wg0vh7us7DhERSTtV6/cT6oQPHIm7ypKIiOSXIyPReInvILJJ2BO+qvNFRPJTFXCw7yCySdgT/jG+AxARkYyZ6zuAbBLahB+JxncBpvuOQ0REMkaFuiShTfioOl9EJN9tq+F5vcKc8A/3HYCIiGTcQb4DyBahTPiRaLwQ2Md3HCIiknFK+IFQJnxgV1wPThERyW/qqR8Ia8I/wHcAIiIyLnaIROOTfQeRDZTwRUQknxngQN9BZIOwJvz9fQcgIiLjRu34hDDhR6LxrYBNfcchIiLjRu34hDDho+p8EZGw2SUSjVf6DsI3JXwREcl3hbjRWaEWxoT/Id8BiIjIuNvddwC+hSrhB5dKnOM7DhERGXe7+Q7At1AlfGB7oNh3ECIiMu5UwvcdwDjbxXcAIiLixfaRaLzUdxA+KeGLiEgYFAM7+Q7Cp7Al/F19ByAiIt6Eulo/bAlfJXwRkfAKdce90CT8SDQ+E5jkOw4REfFmV98B+BSahE/I32gREWEb3wH4FKaEv4PvAERExKtJkWg8tDW9YUr4W/sOQEREvAttKT9MCX9L3wGIiIh3s30H4EuYEv5WvgMQERHvlPDzWTCH/ma+4xAREe+U8PNchPA8VxERGZwSfp5T+72IiIASft5T+72IiABMiETjU30H4YMSvoiIhM0M3wH4EJaEv4XvAEREJGts6jsAH8KS8EN5NiciIgNSws9j03wHICIiWUMJPx9FonEDbOI7DhERyRqhLATmfcLHXRK3xHcQIiKSNUJZwi/yHUCmzS/5/pQqWh5faSd2LLGTWcyUwg+6p5YuslMqlzK5ZrmtndxGSZnvOEVEZNwo4eej3Qvengbstw2LB13HWtZ1UtjYQmliLRUtDXZC2zJb273ETjGLbF3xIltXvshOqVpmJ9U2Ul1rKQhDzYiISL5Sws9TU4ZbwRiqiumqKqaZCTQz06xil0HWtZYui1nVRtHq9ZSvW2OrWlbaiZ1LmWQX2SmFi2xdyWJbV7nYTp6w3E6a3EJpRZqfj4iIjE0o2/CNtdZ3DJkVqzkX+JOvw1tLcyeFja2UJJqoaG601W3LbG3nYjulYLGtK/rA1pUvsVOqltraiauYOKmbgkJfsYqIhEhV/by5630HMZ7CUMKf7PPgxlBRTFdFMS0zq2lhumlgR+oHXNdaui2moZ2iNc2UNa2xlS2rqOlYaid3L7J1hYvslNJFtq5iiZ08YbmtrV1HRfX4PhsRkbxRBSjh55ka3wGkyhgKDHZyGR2Ty+hgkmliS5YNur61tHZR0OBqD8rXN9oJbSvsxK4ldjIf2KlFi2xd+WLX96BmBRMnd1EYhvdbRCQV1cBy30GMpzAkgLxtQzeGsiK6Z1TROqOKVjY1q9mB9wZc11qshTUdFDU2U9aUcLUH7cvsJLvYTilYZOuKPwhqD5bZ2klrqZowzk9HRGQ8VfkOYLwp4YeEMRgDE0vpnFjKOmrNOiJDnNxaS3s3BY2tFK9ZR/m6RlvdtsLWdi61k1lkpxQtsnWli2xd5VI7qXYFtZM6KNJcByKSS5Tw85AS/igYQ0kh3dMqaZtWSRubmDXM4YNB17eWtR0UNbZQ2pSwFc2rqGkLag/MIltXsshOKV9ip1QvtZNq11A1EYwZv2cjIrKR0PWBUsKXtDCGCSV0Tiihkxqzns1ZOei61tLRjWlsozixnvJ1q21V6wpb27HE1R4ULrJ1pYs1MZKIZJZK+HlICT/LGENxIXaTCto3qaCdOpNgmImRmjooXN1K6dq1VDT3TIy02Nbhag/qyhfbydXL7KSJmhhJRFKkhJ+HlPBznDFUl9BVXTKiiZGKV6+nbKOJkT6wU0uCvgc1y+2kSZoYSSS0VKWfhyp9ByDjxxgKDXZKOe1TymlnilnL1iwZdP3kiZHWUrG+0U5oX25ruxbbyWaxrSt639aV9fQ9aKCmVhMjieSN0J3shyHhl/sOQLJX/4mRZpgGduLdAdfdeGKkquaV1HQutZPsYltX0DMx0mI7pXq5rZ28nvLQVRmK5JAw5L8+wvCEw/AcZRwMPDHS0kHXt5aWLgoakydGWm5rO5fYyT0XZSrTxEgi3oSur08YfmDy/GIBkq2MoXyEEyOt7qBodfLESEvtpGBa5bqSRXZKxVI7uVoTI4mkhRJ+Hur2HYDIcIKJkWpL6axNcWKkti4KGtsoTvSfGOkDW1f4gZtWuXKZnTRREyOJDEgJPw8p4UveMYbSIro3LaJt01QnRmqnaNWjZRMW/bO6Zv1z5YV1bQU2Z64zIZJutquiGeb6DmNcKeGLhIAxTCilc8IRbY1bHtHWCMCCkpK35ldXLnmooqJqVWHB9hijDq4SGqYgUeo7hvEWhoSvNnyRAezU3j57p4b22Rc3rKbN0Ppwefnz86urmp4rK9201ZjZGE1/LHktdIXBMCT80L2pIiNVaik7qrll96OaWwBYVViw8taqyrdur6q0C4uLt+42ZhPPIYqkW5fvAMabEr6IbGRKV3fd2YmmurMTTQC8UlLy9vzqysUPVpRXriws3B5jQjdpieQdJfw8pIQvMkY7tLdvvUND+9bfa1hNO7Q9XFH+ws3VVYlny0qntRizrar/JQcp4eehNt8BiOSTEig9srlltyOD6v+GgoJVt1VVvnl7VWX3WyXFW3Ubs6nnEEVSoYSfh5p8ByCSzyZ3d085a23TlLPWuq/aayXFC+dXVy1+oKK8YoWq/73p7uymo7GDoqoiCiuGvwRE1/ouTLGhoCQ0w9PX+g5gvIUh4YfuTRXxaU57x1b/r2H1Vv/PVf+3P1pR/uJ8V/0/tdmY7VT97zQ+1MiSq5ew49U7pryN7basvG0ljQ830pnopGhCEXXH1zH5sMl91lt5x0pW3rqS7tZuKISJ+01k+hnTKSguoLOpk/d/8z4dqzvY7IubUbGVOx9bGV/JpEMnUVIXmjmaVvsOYLwp4YtIxpRAyeHNLbseHlT/NxYUNNzuqv873ywp3rorpNX/HWs6WPbPZSPebsUtK1h520om7j+Riq0rSDyZYOnfl1JUVUTNh9w8SomnEyz/93KmnTKNqjlVrH1xLSvmr6B0k1LqPlzH6kdX093WTflW5ay8dSWzvjqL7vZuOhOdYUr2oISfl1SlL5IlJnV3Tz5jbdO+ZwTV/6+XFL9zc1XVB/dXllcsd9X/obic9dJrl9LdMrL+xJ1rO1l1xyqmnTqNKUdNAVzJ/c1vvMnqR1ZvSPgrblnB5CMnb1inbLMy1r+2nqb/NVH34TraV7RTtXMVldtWsvQ6d/GnxNMJavYO3cSLSvh5SCV8kSy1XXvHlt9uXL3ltxtd9f/jFeUv3lRVmXi6vKwuqP7PuwbltS+sZe1za6k9sJbVj6Sec7rbupn60alMPry3+r6gpICSTUrobOoEXJX/tFOmUbZ5WZ9tTZHBdgZzkHWDKTCYQrNhWrJ1C9Yx87yZY3tiuUcJPw8p4YvkgBIoOaS5ZddDgur/NQUFq2+vqnj9tqrKzjdKSrbsMmaG5xDHrKuliyXXLGHSoZMo36J8RAm/pK6Eug/X9VlmOy2ti1qZsJu7eKIpMFTvXN1nndZFrax/dT11J7hti2qKaF3USkFZAUUTimh+p5nySHkYu1Yo4echVemL5KCJ3d21n167bt9Pr10HwFvFxe/Or6784L7KirJlhYVzMKZ6mF1kneX/Xo7BsMknN2Hts2MvizQ+0kh3SzcT95+40WOti1tpuLuBNU+uoXL7SqYc66r4az5UQ8O9DTS92MT0s6az5rE1TP34VGynxRSFJul3EcLcEIaErxK+SB6Y3dGxxUWNa7a4qHENHdDxeHnZS/OrqxqfKi+rW2/MnGyv/m9+u5nGBxqZ9dVZFJYPP0xuOJ1Nnay4ZQWVcyqp2qFqo8e71nfR+kErtt1iCg22y0Kxa9OfPW823S3dFFYX0vp+Kw33NrDytpXUHlDLjLNzviIlFWsWnLkgdNdZCUPCb/AdgIikVzEUH9zSuvPBLa0AJAoK1sQrK167rbqy87WSki26jMmqBmnbaVl89WJq9qnZqMp9tJb8bQm2zTL9rOkDPl65TSVbXbwV619fT/2l9Sy7YRkzznLJvHhiMUx0w/cm7DGB+l/UM/2M6Sy9bimTj5pM2cyyAfeZR0JXnQ+Q1WfEabLUdwAiklk13d0TT2tat+/1S5Yf+GL9BzNvWbTkvTMSax/ZtKPzKaz1Xsu38s6VdDR0MPWEqXQ2ddLZ1El3m+ul39nUSVfzyCZ9a3yokbXPrmXTT29K6SZDX+W1crtKavauYe0zfV8G221pfd+15RdWFjLpkEmUzSijbWkoJicNZcIPQwlfCV8kZLbq6Jz1zcY1s77ZuIZO6HyivGzB/OqqhifLy6asc9X/Y69TH4F1L6+ju6Wbt7791kaPvf6l16nYtoItv71lSvtqXtjM0muXMvHAidQeWNvnsc6mTlbduYopx0yhaELvz3tRZRG2u28N9rqX1lG9czW2w1JQ7Mp+ptjQ3R6Ky48o4eeplUAHUOw7EBEZf0VQdGBL604Hbqj+N4m7Kitf+09VZcdrpSWRTmM2y3QMm566KV3r+5bi1728jlV3riLyzQiFle78o7utm4LSwSte25a38d6v36N0ZinTT9+4Kr+gvIDGBxuxHZZNP+XmNLLdlqYFTZRvWd5n3cQzCaafNZ22RW0bkvxwx88jSvh5KZawxGqWARn/UotI9qvptjUnN63b5+Qm1/v/neKi92+uqqq/p7KidElR4XYYk/YZaMoj5Rst62jsANjQ4W7ZjctouLeBbX6+DcW1A5dPFl2+iK6mLqYcPWWjXv4T95tIQVEBdcfXsfyfy+lc10nF7Aqanm+ifVl7n7b+9hXtFE8qpqC4gJJpJXS1dLHiPytoW9xG2fS8b78HaPQdgA/5n/CdpSjhi8gAtuzo3Pzrq9ds/vXVa+iCrqfKy16+qbqq4YmysklNBWb78ar+N0WGwgmFGybD6a9zXSctC90cBcv/vXyjxyfuNxGAKcdOoaCkwA29e6GJss3LiFwUoXJ27ySGTf9rovZg1xxQWF7ItJOmsfLWlUw+cjKl04fuE5AnPvAdwGgYN1nCZ4FbrbUrRry9tSEYmRCruRn4qO8wRCS3rHXV/6/fWlXZ9oqr/t/cd0ySFp9acOaC6zJ5AGPMrsAEa+0jwf29gZestS2j3N8RwI+A7YG/W2vPH+k+wlTCFxEZkQndtuakpnV7nxRU/9cXFb1/c3Vl/T2VFSWLiormZKL6X8bFe+NwjOOAbxljplpr24GPA/8EZhljZgGLrLXDDs8wxhwCnAMcHuzjSTvKknpYEv4S3wGISO6LdHZu/tXVic2/ujpBF3Q9U1b68k3VVQ2Pl5dNWltQMAdjwvKbmuvGI+F/DPh/QbIHeAt4J/j/M8AXjDH7WGvfHWon1tqHjDEPA6uAicBZxpi9gNlAPXCBtTalsZRh+XC+7zsAEckvhVC4T2vbjvu0ut/aJmPW3lNZ8fot1VWtr5SWbN5hTMRvhDKIDjJcCDTGHAQY4LKkxUW4pI+1NmaMeQAYsHrfGDMVmAVsCcwBdgMqgS8BDwH3AX8B3k412fcEEAYbD34VEUmjamsnnLhu/YdOXLcegPeLihbdXF357t2VFUWLiormWGMm+o1QAh8sOHNBpicbmAdcaK1NPs4kYBGAMWYLoBBXah/I7rgk/wrupGBzYDFwJ1ADPGitHfHQwrAk/Dd9ByAi4bJ5Z+fMr6xOzPzK6gTd0P1MWekr86urVv63vGxSwlX/a24QP94ZfpXRM8acC0wFngnuVwO7AIcAk4wxnwIqcMm8E3i0/z6stXcBdxljjgOOBY4CXrfW/i5o0/+vMeYJ4Erg8VTb9MPRSx8gVtOAO8MSEfFqnTFN91ZWvHZLdWXrgtLSzTpciU/Gx2ULzlzwxUzs2BgzB7gKeBjXbFAFfAR4FpgL3A/80Fo77EmHMeY83InChbjc9bC1dpvgsQnApbgheouBe4H7rbXXDrnPECX8J4G9fYchItLfoqLCxTdXVb17V1VF4QdFRdtZY2qH30pG6WsLzlzwq3TvNGh3/xNwAfBhoMRa+/vgMQMsB34OzLDWXjjEfuqArwGPWmvvMMaUATOBTwGTgR2BM6y1i4wxe+Cq/huBZ621Q/ZXC0uVPrhqfSV8Eck6Mzu7ZnxpTWLGl9a46v/nykpfvam6auVj5WU1iYKCHVT9n1aZ6tN1IHC6tXa9McYCa5IeOwBXyn8HaB9gWwCMMVsD2wLfA040xjwG1OGGlp8FnAhsgivVY619Dngu1QDDlPDVcU9Esl4BFOzV2rb9XkHv//XGrLuvsuLFW6oqW14qK53ZbkxqV9mRwWQkF1hrb0q6W03fDnnnAFfjrlC70QgBY4yxztvA28HiG4EbjTH/AR7A1RCcjSv9n2GM+Y+1ds1IYgxTwlfHPRHJOZXWVn1k3fq9PhL0/l9cVLjklqqqhXdVVhS9V1y0rTVGfZNS10WGO+0FJgLNAMaYbXFt8WcDJ9OvhB9U9x8CPNh/J0GV/WTgd7gZ9l4BdsKN4/8iI6y1VsIXEckhMzq7pp+/JjH9/KD6/4XS0tfmV1eueKSivGZNQcH2GFPiO8Ys9u6CMxd0jMNx5gD3GGPKcWPxz7bWdhljWtm48/iuwB70S/hB2/3vce313cDLwEnBY9NxPfxHJEwJ/3Xc2d24XgdbRCRTCqBgj7a2OXu0tc0BaDZm/f2V5S/dXFXV/L/S0hntBWYr3zFmmefH6TgHAD8Ffgh8x1rbc9zXgFuMMfW4dvgIrr1+NfCLno2DUv/lQMxaO1ATxEzglpEGFZ5e+gCxmldw1SIiInlvaWHh0v9UVy68o7KyoL64aBtrzBTfMXl20YIzF/ws0wcxxrwKHAqs6D9G3hjzeVxP/um4jn3PAL+21j4VPF4GxIDbrbWPJW1XF/w7EdcBcNfhpuXdKK6QJfxrgE/7DkNEZLxZsP8rLXnjpuqq5Q9XlE9Y7ar/Q3Et3CSHLzhzwQOZPogxZoa1dvEot90DWJA0B3/P8gpcW/7ZwG3W2hNGuu8wVemDq85RwheR0DFgdm1r327XtsbtAFqMaX6govy5m6ur1r1YWjKjraBga98xZphlBEPYxnSgUSb7YNsBY7TWNhtjPodrCvjtaPYdthL+IQzQE1JEJOyWFRYu+09V5cI7qyp5x1X/1w2/VU55e8GZC2b7DsKnsJXwX8Cd5RnfgYiIZJNpXV3Tzk2snXZuYi0W7EulJW/Or65a8nBF+YQGV/1f5jvGMXrWdwC+hauEDxCreRtQz1URkRS1GtPyYEX5qzdXVa57vqx0eltBQS6WlL+x4MwFv/QdhE9hK+GDa8dXwhcRSVGZteXHrm/e49j1zQCsKCxccWtV5VvxqgreKS6e3e3mkc9249J+n83CmvA/6TsIEZFcNbWra+o5ibVTz0msBWBBSclbN7nq/6pVhQXb4yacySbj1mEvm4Ux4T/pOwARkXyyU3v77J0aGmfTAG2G1ocqKp6/uapy7XNlpdNbjZmNm0jGpzcXnLmgyXMM3oU14bcDmn5SRCTNSi1lR69v3v3ooPp/ZWHBytuqKt+8vaqShcXFW3cbs4mHsEJfuocwdtoDiNU8BuzvOwwRkbB5paTkrfnVlUseqKgYz+r/ry04c8GvxuE4WS2MJXyAh1HCFxEZdzu0t8/eoaF99vcaVtMObQ9XlL8wv7pq7XNlpZu0GLNthqr/1ZRLeBP+I8B3fAchIhJmJVB6ZHPLbkc2twDQUFCw6tbqyjfjlZXdb5UUb9VtzKZpOEwTbr760AtrlX4V7qIFunKeiEiWerWkeOHN1VWLHqgor1xRWLg9bj75kYovOHPBh9MeXA4KZ8IHiNU8A+zpOwwRERleO7Q/WlH+yvzqqrXPlpVObTZmuxSr/7++4MwFl2Y8wBwQ1ip9cNX6SvgiIjmgBEoOb27Z7fCg+r+xoKDhdtf7v/PNkuKtuwav/r9//KLMbmEu4Z8A/Md3GCIiMnavlxS/c3NV1Qf3V5ZXLHfV/5XASmCTBWcuCGmi6yvMCb8KaEDj8UVE8ko7tD9eUf7qvRXl9//ogne/4TuebFHgOwBvYol1uGp9ERHJIyVQckhzy64/WtX4qu9Yskl4E75zh+8AREQkY+72HUA2CXvCj/sOQEREMuJlYonFvoPIJuFO+LHEm8DbvsMQEZG0u8t3ANkm3AnfUbW+iEj+udV3ANlGCV/V+iIi+WY58F/fQWQbJXx3IZ31voMQEZG0uYVYott3ENlGCT+WaEM9OUVE8slNvgPIRkr4zj99ByAiImnRCDzoO4hspITv3Iaq9UVE8sFtxBKdvoPIRkr4ALFEMy7pi4hIblN1/iCU8Hvd4DsAEREZkybgHt9BZCsl/F53AgnfQYiIyKjFg47YMgAl/B6xRDtwi+8wRERk1K7xHUA2U8LvS9X6IiK5aSkaYj0kJfy+7gNW+Q5CRERG7FpiiS7fQWQzJfxkbijHtb7DEBGREbvadwDZTgl/Y3/xHYCIiIzIM8QSr/oOItsp4ffnPjSP+w5DRERSdrXvAHKBEv7AVMoXEckNbcD1voPIBUr4A/snGpMvIpILbiWWWO07iFyghD8QN9Xudb7DEBGRYalGNkVK+IPTh0hEJLu9Rixxr+8gcoUS/mBiiReA53yHISIig/qd7wByiRL+0C7zHYCIiAxoDfB330HkEiX8oV0LrPAdhIiIbORKYon1voPIJUr4Q3FXXfqD7zBERKSPbuD3voPINUr4w/sj0OI7CBER2eB2Yol3fQeRa5TwhxNLrELtRCIi2USd9UZBCT81vwKs7yBERISXiSXu8x1ELlLCT0Us8QZwu+8wRESEH/sOIFcp4aful74DEBEJuTeBG30HkauU8FMVSzwMPO07DBGREJtHLNHtO4hcpYQ/Mpf4DkBEJKTeA67xHUQuU8IfiVjiDuAp32GIiITQT4klOn0HkcuU8EfuYt8BiIiEzBLgSt9B5Dol/JGKJe4GHvcdhohIiPwimPlUxkAJf3RUyhcRGR8rgD/7DiIfKOGPhpv04VHfYYiIhMD/EUs0+w4iHyjhj55K+SIimfU2cLnvIPKFEv5oxRIPAg/6DkNEJI99h1iiw3cQ+UIJf2y+jrtMo4iIpNdTxBL/8h1EPlHCH4tY4gV0JT0RkUz4lu8A8o0S/th9B1jvOwgRkTxyO7HEI76DyDdK+GMVSywFfuo7DBGRPNEFRH0HkY+U8NPjF8Ai30GIiOSBq4klXvEdRD5Swk+HWKIF+LbvMEREctxq9FuaMUr46fMP4BnfQYiI5LDvEEus9B1EvlLCT5dYwgJfAazvUEREctCzaJKdjFLCT6dY4gn0gRURGalu4AvEEprXJIOU8NMvCizzHYSISA65nFjiWd9B5Dsl/HSLJdYAF3qOQkQkV6zEzWciGaaEnwmxxI3Anb7DEBHJARcRS6z2HUQYKOFnzhcBXdJRRGRwjwBX+w4iLJTwMyWWqEeX0BURGcx64OxghJOMAyX8zPo18KLnGEQGtKbV0tqp31rxJkossdB3EGFirNUXPqNiNbsBTwHFvkORzLn8uXbOvb0Ve/EEHqrv5NC/Dd6a8+CZFRwSKRrR/leu72b3y9dzzcfKN9p2aVM30y9dt9E2n92tmCtOKGdVczcfuaGFRWu7+ecnytl7pts+el8r5+5Rwha1Ou+XcfcQcJhK9+NrZL86MnKxxAvEai4Bfug7FMmMZeu6uei+1g3350wp4JqPlW203iPvdXHlCx3MqhlZgm3psJz07xYWrR34t/HZJV0UGLjqI2UUmN7lW09yx7nqhQ7Wt1v2mVnIDx5p5/bTimjttCxbZ5XsxQdV5XuihD8+5gHHAfv5DkTS70t3tpLozfdsUlXAp3cu2Wi93z61jtN3KR5Rkm1o7ub461t4b4j5SJ5d0sWcKQWcscvGxwR4u7GbY7cu4uBIERfe5QK98eUOTtlRlU7ixbeIJd71HUQY6fR+PMQSXcAZwMb1rpLTbnujg5te7eTs3YZOnre90cGLy7q5+ODSEe3/2pc6KCqAW0+pGHSdZ5d2s99mhYM+3mWhqMDduoIy1V0LOzl6q8G3EcmQB4HLfAcRVkr448V1Tvma7zAkfZraLF+8o5Uv7FnMAZsPnTx/9Gg7n965mMjEkX3lTti2mAfPrGByhRl0nWeXdPH80i5m/bqJ8h+tZZ8r1nPnWx0bHp9WZViwopvnlnSxSaXhmcVd7LlpIcYMvk+RDFiHqvK9UsIfT7HEX4DbfIch6fGd+1sxwLwjNm6vT/bski6eWtzFBR8auMp9KFvUFlBYMHhirl/TzYr1ljWtli/sWcLPjiijpdPy4etbePyDTgBO2qGY+9/t5Nv3t/GZXYu56sV2PrNbCR1d+t2VcXVeMFxZPFEb/vg7B3gZqPMdiIzek4s6+eOzHdx+ajnVpUOXlH/3dDv7zCxk903TX4VeaOBHh5XyhT1LqC13cZy1azHb/3Edlz7Rzn6bFbHzJoW89aUqEq2WKRWGF5Z18dun2vjhI+18Ztdi/nJCedrjEunnKmKJf/gOIuxUwh9vscQKXNKXHNXRZfncba2ctlMxx84euu2+qc3yz1c6OHvXzHSQ26ymgO8cWLoh2QNUlxo+um0RD9Z3bVg2raqAbacUcuULHXxsu2J++Eg7l80t47qXO3h5RddAuxZJl9eAC3wHIUr4fsQStwK/8R2GjM7PH2/ng0Q33z2whFXN3axq7mZdu3tsVXM3idbeqvL/vNFBRxd8ZLvxrUyrKDY0ttg+1fbd1vLi8i4mlBomlRs+t0cJ29cV8MYqXZFUMqYVOJlYQtOMZwFV6fvzTWAfYG/fgcjI3LOwk0QbbPeH9Rs9VvfzdRw8q5CHzqoE4MZXOjlg80KmVmbm3Pqa/7Xz8opufnpk334EC1Z0M6EUigt7S/53vtXJsVsX0dJpKQu++eVFhuYOteVLxlxILLHAdxDiKOH7Ekt0EKs5GXgBqPUdjqTul0eVsbq1b5K8Z2EnP3+8nXtPr6C2zCXZ1k7Lfe90DjkUb327pbJk9L3lE23w26fb+fweJWwVTLTz2Pud3L2wkxPn9P16/+vVTv784TIWrOimOejEv75jbMcXGcK/iCX+7DsI6aWE71Ms8R6xmtNxPff1q5sj9pi+cee7RWtdtfgRW/Z+pR7/oIvWTgYdI/+te1v57VPtvPOVKqZXj64G4Ixdivnxo20cdPV6Pr5dEU3t8K9XO6gtM/zwsN4TjXdWdzNzgqG0yLDN5AISbZYfPNzGKyu62b5OLXuSdu8Cn/MdhPSlb7pvsUQc+JHvMCT9Hny3k6IC2GuAEwSA0kKoqzR0j6FGfUKp4eGzKthlk0KufLGD+FudfHxOMS9/sZJtJvce9/Y3O/jc7iUbtvnZEaX86sk2vrx3CdtN0QQ8klYtwInEEgnfgUhfunhONojVFAB3AEf7DkVEZIxOJZa4wXcQsjGV8LNBLNENfAp4z3coIiJj8FMl++ylhJ8tYokG4AQ0376I5KY7gO/4DkIGp4SfTWKJl4BTAQ2MFpFc8gZwWlBbKVlKCT/bxBK348boi4jkggTwEXXSy35K+NkolrgUuNx3GCIiw+jGlezf8B2IDE8JP3udD9zvOwgRkSF8lVjiDt9BSGo0LC+bxWomAk8C23qORESkv58TS3zLdxCSOpXws1kssQb4MNDgORIRkWT/AC7yHYSMjEr4uSBWsw9wH1DpOxQRCb37gOOIJTp8ByIjo4SfK2I1RwK3AyW+QxGR0HoROIhYosl3IDJyqtLPFbHEvbjZ+DTOVUR8eA9Xsleyz1FK+Lkklvg3cK7vMEQkdBqAY4gllvoOREZPCT/XxBJXAFHfYYhIaKwGjiSWeN13IDI2Svi5KJb4KfAz32GISN5LAEcTS7zgOxAZO3Xay2Wxmj+hKn4RyYwmXLJ/wncgkh4q4ee2LwCX+Q5CRPLOemCukn1+UQk/H8Rqfgt8yXcYIpIXWnDJ/kHfgUh6qYSfD2KJLwO/9h2GiOS8VtyV75Ts85ASfr6IJb4K/Nx3GCKSs5pxyf5e34FIZijh5xN3IYuf+A5DRHLOGtzQu3t8ByKZo4Sfb2KJ7wA/8B2GiOSM5cDBxBKP+w5EMkud9vJVrOZC4FLAeI5ERLJXPa5k/7bvQCTzlPDzWazmZODv6II7IrKx13DJfrHvQGR8KOHnu1jNocAtwATPkYhI9ngWNzd+g+9AZPyoDT/fueE1BwJLfIciIlnhXuAwJfvwUcIPg1jiJWA/QBe/EAm3P6FL3IaWqvTDJFYzGbgN2Nd3KCIyrrqBrxNL/Np3IOKPSvhh4qrwDgOu8R2KiIybdcBHlexFJfywitV8HfgpUOg7FBHJmEXA8cQSL/oORPxTwg+zWM3RwA3ARM+RiEj6PYdL9kt9ByLZQVX6YRZL3A3sjTrzieSb64CDlOwlmUr4ArGaCbgfiLm+QxGRMWkHvkYs8QffgUj2UQlfIJZYC5yAu/COzgBFctMHuFK9kr0MSCV86StWcwxuOt4636GISMruBU4jlljlOxDJXirhS1+xxF3ArsDDniMRkeFZ3NUxj1Gyl+GohC8Di9UUAt8HvotODEWyUSNwOrHEHb4DkdyghC9Di9UcBlwLbOo7FBHZ4F7gLGIJXSNDUqaSmwwtlngAV8V/j+dIRARagQuBo5XsZaRUwpfUxGoMcAEwD6jwHI1IGL0IfJpY4hXfgUhuUsKXkYnVbA1cBRzgOxSRkOgGfgF8j1ii3XcwkruU8GXkYjUFuGrFHwFlfoMRyWvvAWcQSzziOxDJfUr4Mnqxmm2Bq4F9PEcikm8s8GfgomBiLJExU8KXsXHD974BXAKUeo5GJB+8AXyOWOJR34FIflHCl/SI1WwD/AE4wncoIjmqA/g58H/EEm2+g5H8o4Qv6RWrOQX4JTDddygiOeRR4DxiiVd9ByL5S+PwJb1iiRuA7YBfA11+gxHJeo3AOcDBSvaSaSrhS+bEanYB/gjs5zsUkSzTCfwJuJhYotF3MBIOSviSWW7CnrNwE/ZM9RuMSFa4E/g6scRrvgORcFHCl/ERq6kGvgl8Daj0HI2ID6/iEv1dvgORcFLCl/EVq9kUN4TvbKDQczQi46EBiAF/Ipbo9ByLhJgSvvgRq5kD/BQ43ncoIhnSguvD8iNiidW+gxFRwhe/YjUH4cYef8h3KCJp0oabJe8nxBLLfAcj0kMJX7JDrOZ44HvAXr5DERmlduCvuBL9Yt/BiPSnhC/ZJVZzNC7x7+87FJEUdeKuKfFDYon3PMciMiglfMlOsZpDcYn/UN+hiAyiHbgWV6J/x3cwIsNRwpfsFqvZH5f4j/YdikhgNW7SnN8RSyz1HYxIqpTwJTe4Wfu+DJwGlHmORsLpXdyU0X8llljvORaREVPCl9wSq5kCfB74IjDDczQSDk/jLgh1E7GErg8hOUsJX3JTrKYI+ATwFWAfz9FI/mkDbsJNlqPr0kteUMKX3Ber2Qu4AHcCUOE5GsltrwF/Af5OLNHgOxiRdFLCl/zh5us/CfgMGtYnqWsB/g1cTizxmO9gRDJFCV/yU6xmNu4qfWeitn4Z2PO48fPXEEus8RuKSOYp4Ut+i9UUAEfiSv3Hoyr/sHsNuAG4gVjiTd/BiIwnJXwJj1hNBXAccCLwYaDKb0AyTt4FbsQl+f/5DkbEFyV8CadYTRluMp8TgROAGr8BSZq9A9yGS/JP+g5GJBso4YvEakqAw4GP404CNvMbkIxCJ/AYEAduJ5Z43XM8IllHCV+kv1jNHFziPwo4GLX7Z6uVwJ24JH83sUTCczwiWU0JX2QosZpS4EBc8j8a2NlvQKG2BngUeAR4GHiOWKLba0QiOUQJX2Qk3NS++wL7Bbe9gHKvMeWvVfQm90eAl5TgRUZPCV9kLGI1xcCu9J4A7AfM9BlSjmoBXgKew42PfxJ4lVhCP1AiaaKEL5JusZppuJOAXYFdgttsoMhfUFllHfAiLrE/j0vyr+nCNCKZpYQvMh7cSIDtgB2AHYFtgS2CW63HyDKlG3gfeDPp9kbw931VzYuMPyV8Ed9iNTXAlvSeAGwR3J8JTAWmAIXe4htYE7Ck320x8AEuqb9NLNHqLzwR6U8JXyTbxWoMrhZgKlAX3Hr+r8V1GqwI/vb/vxwowZW4u4b42wGsBRL9/ib/3wgsBRYTS6zL7JMWkXRTwhcREQmBAt8BiIiISOYp4YuIiISAEr6IiEgIKOGLiIiEgBK+iIhICCjhi4iIhIASvoiISAgo4UuoGGPqjDFHGWOKU1x/gjGmpt+yMmPM940xEzMSpIhIBijhS94yxpQaY041xtQlLT4NuB04O8XdnAW8bIwpS1o2C/g+8BljzAXGmG+negIhIuKLZtqTvGaM+R1wCu6iNQXAHcD51tonUth2GvAA8AwwGVgEfAl3wnBdsFoxsNBa+2D6oxcRSR8lfMl7xpi9gYXAUbgLu2wJvGytfXWIbeqAG4FLgcettY3B8m8Cj+FOIu6z1t6W4fBFRNJC1+eWvGWMOQHYDZiOS/J1wCrgdaAdGDDhG2OKgLnAicA04C1jzF7AscC/rLX1xpgu4E/GmDustbqOu4hkPZXwJW8ZYzYFdsaV6i8DbrTWXjXK/awE5uOq9xfjqvj/Zq1dkb6IRUQyRyV8yWctuOu27w3sBHQZYy7EXbP9VGtt01AbG2M2B34MRHDfla2ANtwlZZcDVYASvojkBJXwJW8FCfuLuCR9EvBR4C1rbfco93c1rk1/KTAF10QwE1ftvwq4zlrbOebARUQyQAlf8p4x5kvAPsBtwLbAjsAmwE3W2t/0W3d/4ACgBKjBJfZKXAl/Iq7tf1fgH8BaoAMoDNa53lr7Yqafj4jIaKhKX/KOMWY74Ke4kncLsD+uDb8GeB64B2jADbPr7xncyYABXgPet9au67f/J4FHgFeANdbalZl5JiIi6aMSvuSlYCIcA3wM+Ii19rR+j5Wl0IZ/Pu4koQSoxlXhl+P6BDTgOvK1AOdZa5dm4nmIiKSLEr7kLWPMFNzEOd8A9sRV5++AS9yPAGfYIb4AxpitgcOAPYCfA4usta3GmCuBK621j2X4KYiIpI2q9CUvBaX464ALgMeBZcBU4C5r7XdT2Ye19m3gbWPMY8Bh1trLg4cKMxCyiEhGKeFL3jHGlAJXAn+w1j4SLH7JGPM6rhPeSJ2Gm7ynRzm6DoWI5BglfMkrxphNcBe2uQx41xizJa4dfjKud33EGPNXYDPc+PpS4C/W2h8Otk9r7fvA+8aYPXCz822LSvkikmOU8CVvBFPi7gR8GeiZNKcQN4/+ItxFb5biOtw14zrjlZJ68q7FTbhTgZu9T0QkZ6jTnsgIGGPOAKZaa3/hOxYRkZFQwhcREQkBdTwSEREJASV8ERGREFDCFxERCQElfBERkRBQwhcREQkBJXwREZEQUMIXEREJASV8ERGREFDCFxERCQElfBERkRBQwhcREQkBJXwREZEQUMIXEREJASV8ERGREFDCFxERCQElfBERkRBQwhcREQkBJXwREZEQUMIXEREJASV8ERGREFDCFxERCQElfBERkRBQwhcREQkBJXwREZEQUMIXEREJASV8ERGREFDCFxERCQElfBERkRBQwhcREQkBJXwREZEQ+P93fxXWyY0mywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 等宽分箱操作\n",
    "\n",
    "test_m['labels1'] = pd.cut(test_m['男引体'],\n",
    "                              bins=3, # 分成3份\n",
    "                              labels=['差等','中等','优秀'], # 区间标签\n",
    "                              right=True) # 区间是闭区间\n",
    "data = test_m['labels1'].value_counts()\n",
    "\n",
    "plt.figure(figsize=(9,9))\n",
    "\n",
    "_ = plt.pie(data,autopct='%0.2f%%',labels=data.index,textprops = {'family':'KaiTi','fontsize':18})\n",
    "\n",
    "_ = plt.title(label='    男引体测试成绩分布情况    ',\n",
    "             fontsize = 32,\n",
    "             pad=30,\n",
    "             family = 'KaiTi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0b0e088b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 等宽分箱操作\n",
    "\n",
    "test_m['labels'] = pd.cut(test_m['男1000米跑'],\n",
    "                              bins=3, # 分成3份\n",
    "                              labels=['优秀','中等','差等'], # 区间标签\n",
    "                              right=True) # 区间是闭区间\n",
    "data = test_m['labels'].value_counts()\n",
    "\n",
    "plt.figure(figsize=(9,9))\n",
    "\n",
    "_ = plt.pie(data,autopct='%0.2f%%',labels=data.index,textprops = {'family':'KaiTi','fontsize':18})\n",
    "\n",
    "_ = plt.title(label='    男1000米跑测试成绩分布情况    ',\n",
    "             fontsize = 32,\n",
    "             pad=30,\n",
    "             family = 'KaiTi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8d65376f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2、 对女 800 米跑、 女跳远进行直方图绘制统计各分数段人数， 分成 4 份\n",
    "test_w = pd.read_excel('./18级高一体测成绩汇总.xls',sheet_name=1)\n",
    "# 加载评分标准表\n",
    "score_table = pd.read_excel('./体侧成绩评分表.xls',header = [0,1])\n",
    "\n",
    "test_w.drop_duplicates(inplace=True)\n",
    "test_w.reset_index(inplace=True) # 行索引重置：0~最后，从0开始编号\n",
    "\n",
    "# 女800米跑数据类型转换\n",
    "# 获取分钟数\n",
    "score_table_min_w = score_table['女800米跑']['成绩'].str.replace('\"','')\\\n",
    "                                                   .map(convert).str.extract(r'(\\d+)\\'(\\d+)').applymap(lambda x:int(x))[0]\n",
    "# 获取秒转换为分钟数\n",
    "score_table_sec_w = score_table['女800米跑']['成绩'].str.replace('\"','')\\\n",
    "                                                   .map(convert).str.extract(r'(\\d+)\\'(\\d+)').applymap(lambda x:int(x))[1] / 60\n",
    "score_table.loc[:,('女800米跑','成绩')] = score_table_min_w + score_table_sec_w.round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fa5ca620",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>女800米跑分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>9.32</td>\n",
       "      <td>185.0</td>\n",
       "      <td>16</td>\n",
       "      <td>48</td>\n",
       "      <td>3775</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.3</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>11.44</td>\n",
       "      <td>148.0</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>3683</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.6</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>13.40</td>\n",
       "      <td>150.0</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>3331</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>9.52</td>\n",
       "      <td>172.0</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
       "      <td>3701</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.7</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>9.79</td>\n",
       "      <td>145.0</td>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>3592</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.9</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  班级 性别  女800米跑  女50米跑    女跳远  女体前屈  女仰卧  女肺活量     身高    体重  BMI  \\\n",
       "0      0   1  女    3.22   9.32  185.0    16   48  3775  163.0  51.3    0   \n",
       "1      1   1  女    4.59  11.44  148.0     9   29  3683  163.0  66.6    0   \n",
       "2      2   1  女    3.46  13.40  150.0     7   40  3331  157.0  60.0    0   \n",
       "3      3   1  女    3.39   9.52  172.0    21   46  3701  160.0  50.7    0   \n",
       "4      4   1  女    3.43   9.79  145.0     8   34  3592  167.0  63.9    0   \n",
       "\n",
       "   女800米跑分数  \n",
       "0       100  \n",
       "1        62  \n",
       "2        95  \n",
       "3       100  \n",
       "4        95  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['女800米跑']\n",
    "\n",
    "for col in cols:\n",
    "    def convert(x):\n",
    "        if x == 0: # 说明没有参加体能测试\n",
    "            return 0\n",
    "        for i in range(score_table[col].shape[0]):\n",
    "            if x <= score_table[col]['成绩'][i]:\n",
    "                return score_table[col]['分数'][i]\n",
    "        return 0 # 说明跑的太慢了\n",
    "    test_w[col + '分数'] = test_w[col].transform(convert)\n",
    "test_w.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7146e2ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_w['labels'] = pd.cut(test_w['女800米跑分数'],\n",
    "                              bins=4, # 分成4份\n",
    "                              labels=['不及格','及格','中等','优秀'], # 区间标签\n",
    "                              right=True) # 区间是闭区间\n",
    "data = test_w['labels'].value_counts()\n",
    "plt.figure(figsize=(12,9))\n",
    "\n",
    "plt.rcParams['font.family'] = 'KaiTi'\n",
    "plt.rcParams['font.size'] = 18\n",
    "plt.bar(x= data.index[::-1],height=data[::-1],color = '#3c7f99')\n",
    "\n",
    "_ = plt.title(label='    女800米跑分数分布情况    ',pad=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a6b131c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>labels</th>\n",
       "      <th>女跳远分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>9.32</td>\n",
       "      <td>185.0</td>\n",
       "      <td>16</td>\n",
       "      <td>48</td>\n",
       "      <td>3775</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.3</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>优秀</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>11.44</td>\n",
       "      <td>148.0</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>3683</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.6</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>中等</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>13.40</td>\n",
       "      <td>150.0</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>3331</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>优秀</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>9.52</td>\n",
       "      <td>172.0</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
       "      <td>3701</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.7</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>优秀</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>9.79</td>\n",
       "      <td>145.0</td>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>3592</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.9</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>优秀</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  班级 性别  女800米跑  女50米跑    女跳远  女体前屈  女仰卧  女肺活量     身高    体重  BMI  \\\n",
       "0      0   1  女    3.22   9.32  185.0    16   48  3775  163.0  51.3    0   \n",
       "1      1   1  女    4.59  11.44  148.0     9   29  3683  163.0  66.6    0   \n",
       "2      2   1  女    3.46  13.40  150.0     7   40  3331  157.0  60.0    0   \n",
       "3      3   1  女    3.39   9.52  172.0    21   46  3701  160.0  50.7    0   \n",
       "4      4   1  女    3.43   9.79  145.0     8   34  3592  167.0  63.9    0   \n",
       "\n",
       "   女800米跑分数 labels  女跳远分数  \n",
       "0       100     优秀     85  \n",
       "1        62     中等     60  \n",
       "2        95     优秀     60  \n",
       "3       100     优秀     76  \n",
       "4        95     优秀     50  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['女跳远']\n",
    "\n",
    "for col in cols:\n",
    "    def convert(x):\n",
    "        if x == 0: # 说明没有参加体能测试\n",
    "            return 0\n",
    "        for i in range(score_table[col].shape[0]):\n",
    "            if x >= score_table[col]['成绩'][i]:\n",
    "                return score_table[col]['分数'][i]\n",
    "        return 0 # 说明跑的太慢了\n",
    "    test_w[col + '分数'] = test_w[col].apply(convert)\n",
    "test_w.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c8a1897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_w['labels1'] = pd.cut(test_w['女跳远分数'],\n",
    "                              bins=4, # 分成4份\n",
    "                              labels=['不及格','及格','中等','优秀'], # 区间标签\n",
    "                              right=True) # 区间是闭区间\n",
    "data = test_w['labels1'].value_counts()\n",
    "plt.figure(figsize=(12,9))\n",
    "\n",
    "plt.rcParams['font.family'] = 'KaiTi'\n",
    "plt.rcParams['font.size'] = 18\n",
    "plt.bar(x= data.index[::-1],height=data[::-1],color = '#3c7f99')\n",
    "\n",
    "_ = plt.title(label='    女跳远分数分布情况    ',pad=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b34298c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3、 使用嵌套饼图对比男女生体重指数进行比例统计， 分为正常、 低体重、 超重、 肥胖\n",
    "\n",
    "# BMI=体重(以千克为单位)除以身高的平方(以米为单位)\n",
    "test_m['BMI'] = (test_m['体重'] / ((test_m['身高'] / 100) ** 2)).round(1)\n",
    "test_w['BMI'] = (test_w['体重'] / ((test_w['身高'] / 100) ** 2)).round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "681b5381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>labels</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>labels1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>9.32</td>\n",
       "      <td>185.0</td>\n",
       "      <td>16</td>\n",
       "      <td>48</td>\n",
       "      <td>3775</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.3</td>\n",
       "      <td>19.3</td>\n",
       "      <td>100</td>\n",
       "      <td>优秀</td>\n",
       "      <td>85</td>\n",
       "      <td>优秀</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>11.44</td>\n",
       "      <td>148.0</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>3683</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.6</td>\n",
       "      <td>25.1</td>\n",
       "      <td>62</td>\n",
       "      <td>中等</td>\n",
       "      <td>60</td>\n",
       "      <td>中等</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>13.40</td>\n",
       "      <td>150.0</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>3331</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>95</td>\n",
       "      <td>优秀</td>\n",
       "      <td>60</td>\n",
       "      <td>中等</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>9.52</td>\n",
       "      <td>172.0</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
       "      <td>3701</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.7</td>\n",
       "      <td>19.8</td>\n",
       "      <td>100</td>\n",
       "      <td>优秀</td>\n",
       "      <td>76</td>\n",
       "      <td>优秀</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>9.79</td>\n",
       "      <td>145.0</td>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>3592</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.9</td>\n",
       "      <td>22.9</td>\n",
       "      <td>95</td>\n",
       "      <td>优秀</td>\n",
       "      <td>50</td>\n",
       "      <td>及格</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>588</td>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>9.60</td>\n",
       "      <td>150.0</td>\n",
       "      <td>24</td>\n",
       "      <td>41</td>\n",
       "      <td>2255</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>19.6</td>\n",
       "      <td>90</td>\n",
       "      <td>优秀</td>\n",
       "      <td>60</td>\n",
       "      <td>中等</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>589</td>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>10.18</td>\n",
       "      <td>150.0</td>\n",
       "      <td>13</td>\n",
       "      <td>36</td>\n",
       "      <td>2937</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.7</td>\n",
       "      <td>21.5</td>\n",
       "      <td>76</td>\n",
       "      <td>优秀</td>\n",
       "      <td>60</td>\n",
       "      <td>中等</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>590</td>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>10.18</td>\n",
       "      <td>152.0</td>\n",
       "      <td>15</td>\n",
       "      <td>35</td>\n",
       "      <td>2592</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.6</td>\n",
       "      <td>17.9</td>\n",
       "      <td>95</td>\n",
       "      <td>优秀</td>\n",
       "      <td>62</td>\n",
       "      <td>中等</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>591</td>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>9.67</td>\n",
       "      <td>165.0</td>\n",
       "      <td>10</td>\n",
       "      <td>41</td>\n",
       "      <td>1829</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.6</td>\n",
       "      <td>18.4</td>\n",
       "      <td>74</td>\n",
       "      <td>中等</td>\n",
       "      <td>70</td>\n",
       "      <td>中等</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>592</td>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>9.09</td>\n",
       "      <td>180.0</td>\n",
       "      <td>10</td>\n",
       "      <td>46</td>\n",
       "      <td>2962</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.3</td>\n",
       "      <td>21.1</td>\n",
       "      <td>64</td>\n",
       "      <td>中等</td>\n",
       "      <td>80</td>\n",
       "      <td>优秀</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>591 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  班级 性别  女800米跑  女50米跑    女跳远  女体前屈  女仰卧  女肺活量     身高    体重   BMI  \\\n",
       "0        0   1  女    3.22   9.32  185.0    16   48  3775  163.0  51.3  19.3   \n",
       "1        1   1  女    4.59  11.44  148.0     9   29  3683  163.0  66.6  25.1   \n",
       "2        2   1  女    3.46  13.40  150.0     7   40  3331  157.0  60.0  24.3   \n",
       "3        3   1  女    3.39   9.52  172.0    21   46  3701  160.0  50.7  19.8   \n",
       "4        4   1  女    3.43   9.79  145.0     8   34  3592  167.0  63.9  22.9   \n",
       "..     ...  .. ..     ...    ...    ...   ...  ...   ...    ...   ...   ...   \n",
       "586    588  17  女    3.51   9.60  150.0    24   41  2255  158.0  49.0  19.6   \n",
       "587    589  17  女    4.00  10.18  150.0    13   36  2937  161.0  55.7  21.5   \n",
       "588    590  17  女    3.45  10.18  152.0    15   35  2592  165.0  48.6  17.9   \n",
       "589    591  17  女    4.01   9.67  165.0    10   41  1829  154.0  43.6  18.4   \n",
       "590    592  17  女    4.48   9.09  180.0    10   46  2962  162.0  55.3  21.1   \n",
       "\n",
       "     女800米跑分数 labels  女跳远分数 labels1  \n",
       "0         100     优秀     85      优秀  \n",
       "1          62     中等     60      中等  \n",
       "2          95     优秀     60      中等  \n",
       "3         100     优秀     76      优秀  \n",
       "4          95     优秀     50      及格  \n",
       "..        ...    ...    ...     ...  \n",
       "586        90     优秀     60      中等  \n",
       "587        76     优秀     60      中等  \n",
       "588        95     优秀     62      中等  \n",
       "589        74     中等     70      中等  \n",
       "590        64     中等     80      优秀  \n",
       "\n",
       "[591 rows x 16 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d7471f54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "正常     356\n",
       "超重      59\n",
       "肥胖      45\n",
       "低体重     13\n",
       "Name: BMI等级, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 男生体重指数\n",
    "def convert1(x):\n",
    "    if 16.5 <= x <=23.2:\n",
    "        return '正常'\n",
    "    elif x <= 16.4:\n",
    "        return '低体重'\n",
    "    elif 23.3 <= x <= 26.3:\n",
    "        return '超重'\n",
    "    else:\n",
    "        return '肥胖'\n",
    "test_m['BMI等级'] = test_m['BMI'].apply(convert1)\n",
    "data1 = test_m['BMI等级'].value_counts()\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cd8aee1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "正常     445\n",
       "超重      83\n",
       "肥胖      53\n",
       "低体重     10\n",
       "Name: BMI等级, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 女生体重指数\n",
    "def convert1(x):\n",
    "    if 16.5 <= x <=22.7:\n",
    "        return '正常'\n",
    "    elif x <= 16.4:\n",
    "        return '低体重'\n",
    "    elif 22.8 <= x <= 25.2:\n",
    "        return '超重'\n",
    "    else:\n",
    "        return '肥胖'\n",
    "test_w['BMI等级'] = test_w['BMI'].apply(convert1)\n",
    "data2 = test_w['BMI等级'].value_counts()\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "500f4be8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 外圈\n",
    "plt.figure(figsize=(9,9))\n",
    "_ = plt.pie(data1,radius=1,\n",
    "            autopct='%0.2f%%',\n",
    "            pctdistance=0.85,\n",
    "            labels = data1.index,\n",
    "            wedgeprops={'linewidth':5, # 间隔宽度\n",
    "                        'width':0.3, # 饼图宽度\n",
    "                        'edgecolor':'white'}, # 间隔的颜色\n",
    "            textprops = {'family':'KaiTi','fontsize':18}) #设置字体样式\n",
    "# 内圈\n",
    "_ = plt.pie(data2,radius=0.7,\n",
    "            autopct='%0.2f%%',\n",
    "            pctdistance=0.55,\n",
    "            wedgeprops={'linewidth':5, # 间隔宽度\n",
    "                        'width':0.7, # 饼图宽度\n",
    "                        'edgecolor':'white'}) # 间隔的颜色\n",
    "\n",
    "plt.rcParams['font.family'] = 'KaiTi' # 全局设置字体\n",
    "plt.rcParams['font.size'] = 18\n",
    "_ = plt.legend(data2.index,title = 'BIM等级',prop = 'KaiTi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83bde0b3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
